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/*! \file
    \brief Templates calculating the address and predicates to the load of scale
   and bias vectors.

    This iterator uses masks to guard out-of-bounds accesses.

    A precomputed "Params" object minimizes the amount of state that must be
   stored in registers, and integer addition is used to advance the pointer
   through memory.
*/

#pragma once

#include "cutlass/array.h"
#include "cutlass/coord.h"
#include "cutlass/cutlass.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/layout/pitch_linear.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/predicate_vector.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/tensor_view.h"
#include "cutlass/conv/threadblock/conv2d_params.h"

////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace conv {
namespace threadblock {

////////////////////////////////////////////////////////////////////////////////

/// PredicatedScaleBiasVectorAccessIterator
///
template <typename ThreadblockShape, typename Element, typename Layout>
class PredicatedScaleBiasVectorAccessIterator;

////////////////////////////////////////////////////////////////////////////////

/// Specialization of PredicatedTileAccessIterator for fprop pitch-linear data.
///
template <typename ThreadblockShape_, typename Element_>
class PredicatedScaleBiasVectorAccessIterator<ThreadblockShape_, Element_,
                                              layout::PitchLinear> {
public:
    using ThreadblockShape = ThreadblockShape_;
    using Element = Element_;
    using Layout = layout::PitchLinear;

    using Index = typename Layout::Index;
    using LongIndex = typename Layout::LongIndex;

    using TensorRef = TensorRef<Element, Layout>;
    using TensorView = TensorView<Element, Layout>;
    using TensorCoord = typename Layout::TensorCoord;

    using ConstPointer = const Element*;
    using NonConstPointer = typename platform::remove_const<Element>::type*;

    static int const kElementsPerAccess = 128 / sizeof_bits<Element>::value;
    static int const kThreads =
            ThreadblockShape::kContiguous / kElementsPerAccess;

    using AccessType = AlignedArray<Element, kElementsPerAccess>;

    using Params = PredicatedScaleBiasVectorAccessIteratorParams;

private:
    /// Internal pointer type permits fast address arithmetic
    using BytePointer = char*;

private:
    //
    // Data members
    //

    /// Parameters object with precomputed internal state
    Params const& params_;

    /// Internal pointer to first access of tile
    BytePointer pointer_;

    /// Size of tensor
    Conv2dProblemSize problem_size_;

    int filter_c_;
    int filter_r_;
    int filter_s_;

    TensorCoord thread_offset_;

public:
    /// Constructs a TileIterator from its precomputed state, threadblock
    /// offset, and thread ID
    CUTLASS_HOST_DEVICE
    PredicatedScaleBiasVectorAccessIterator(
            /// Precomputed parameters object
            Params const& params,
            /// Extent of tensor
            Conv2dProblemSize const& problem_size,
            /// Pointer to the start of the scale vector
            ConstPointer scale_pointer,
            /// Pointer to the start of the bias vector
            ConstPointer bias_pointer,
            /// ID of each participating thread
            int thread_id,
            /// Initial offset of threadblock
            TensorCoord const& threadblock_offset)
            : params_(params),
              problem_size_(problem_size),
              filter_c_(0),
              filter_r_(0),
              filter_s_(0) {
        pointer_ = (thread_id < kThreads)
                           ? reinterpret_cast<BytePointer>(
                                     const_cast<NonConstPointer>(scale_pointer))
                           : reinterpret_cast<BytePointer>(
                                     const_cast<NonConstPointer>(bias_pointer));

        // Per-thread offset in logical coordinates of tensor
        int thread_base = (thread_id < kThreads) ? 0 : kThreads;

        thread_offset_ =
                threadblock_offset +
                TensorCoord((thread_id - thread_base) * kElementsPerAccess, 0);

        set_iteration_index(0);
    }

    /// Construct a PredicatedTileAccessIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    PredicatedScaleBiasVectorAccessIterator(
            /// Precomputed parameters object
            Params const& params,
            /// Extent of tensor
            Conv2dProblemSize const& problem_size,
            /// Pointer to start of scale vector
            ConstPointer scale_pointer,
            /// Pointer to start of scale vector
            ConstPointer bias_pointer,
            ///< ID of each participating thread
            int thread_id)
            : PredicatedScaleBiasVectorAccessIterator(
                      params, problem_size, scale_pointer, bias_pointer,
                      thread_id, make_Coord(0, 0)) {}

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {}

    /// Advances an iterator along logical dimensions of matrix in units of
    /// whole threadblock tiles
    CUTLASS_DEVICE
    void add_tile_offset(TensorCoord const& tile_offset) {
        thread_offset_ =
                thread_offset_ + TensorCoord(ThreadblockShape::kContiguous *
                                                     tile_offset.contiguous(),
                                             0);
    }

    /// Returns a pointer
    CUTLASS_HOST_DEVICE
    AccessType* get() const {
        return reinterpret_cast<AccessType*>(pointer_ +
                                             (thread_offset_.contiguous() *
                                              sizeof_bits<Element>::value / 8));
    }

    /// Increment and return an instance to self.
    CUTLASS_HOST_DEVICE
    PredicatedScaleBiasVectorAccessIterator& operator++() { return *this; }

    /// Increment and return an instance to self.
    CUTLASS_HOST_DEVICE
    void advance() {
        // moves to the next tile
        ++filter_s_;
        if (filter_s_ == problem_size_.S) {
            filter_s_ = 0;
            ++filter_r_;

            if (filter_r_ < problem_size_.R) {
            } else {
                filter_r_ = 0;
                add_tile_offset(TensorCoord(1, 0));
            }
        }
    }

    /// Increment and return an instance to self.
    CUTLASS_DEVICE
    PredicatedScaleBiasVectorAccessIterator operator++(int) {
        PredicatedScaleBiasVectorAccessIterator self(*this);
        operator++();
        return self;
    }

    /// Returns whether access is valid or not
    CUTLASS_HOST_DEVICE
    bool valid() {
        uint32_t enabled = 0;

#if defined(_MSC_VER) || (__CUDACC_VER_MAJOR__ < 11)
        enabled = threadIdx.x < kThreads * 2;
#else
        asm volatile(
                "{\n"
                "  .reg .u32 tid_reg;\n"
                "  .reg .pred p;\n"
                "  mov.u32 tid_reg, %%tid.x;\n"
                "  setp.lt.u32 p, tid_reg, %1;\n"
                "  selp.u32 %0, 1, 0, p;\n"
                "}\n"
                : "+r"(enabled)
                : "n"(kThreads * 2));
#endif

        return ((thread_offset_.contiguous() < problem_size_.C) && enabled);
    }
};

////////////////////////////////////////////////////////////////////////////////

/// Specialization of PredicatedTileAccessIterator for row-major data.
///
/// Satisfies: ForwardTileIteratorConcept |
///            ReadableContiguousTileIteratorConcept |
///            WriteableContiguousTileIteratorConcept |
///            MaskedTileIteratorConcept
///
template <typename ThreadblockShape_, typename Element_>
class PredicatedScaleBiasVectorAccessIterator<ThreadblockShape_, Element_,
                                              layout::RowMajor> {
public:
    using ThreadblockShape = ThreadblockShape_;
    using Element = Element_;
    using Layout = layout::RowMajor;

    using Index = typename Layout::Index;
    using LongIndex = typename Layout::LongIndex;

    using TensorRef = TensorRef<Element, Layout>;
    using TensorView = TensorView<Element, Layout>;
    using TensorCoord = typename Layout::TensorCoord;

    using ConstPointer = const Element*;
    using NonConstPointer = typename platform::remove_const<Element>::type*;

    using UnderlyingIterator = PredicatedScaleBiasVectorAccessIterator<
            layout::PitchLinearShape<ThreadblockShape::kColumn,
                                     ThreadblockShape::kRow>,
            Element, layout::PitchLinear>;

    using AccessType = typename UnderlyingIterator::AccessType;
    static int const kElementsPerAccess =
            UnderlyingIterator::kElementsPerAccess;

    using Params = PredicatedScaleBiasVectorAccessIteratorParams;

private:
    //
    // Data members
    //

    /// Underlying pitch-linear tile iterator
    UnderlyingIterator iterator_;

public:
    /// Constructs a TileIterator from its precomputed state, threadblock
    /// offset, and thread ID
    CUTLASS_HOST_DEVICE
    PredicatedScaleBiasVectorAccessIterator(
            ///< Precomputed parameters object
            Params const& params,
            ///< Extent of tensor
            Conv2dProblemSize const& problem_size,
            ///< Pointer to the start of the scale vector
            ConstPointer scale_pointer,
            ///< Pointer to the start of the bias vector
            ConstPointer bias_pointer,
            ///< ID of each participating thread
            int thread_id,
            ///< Initial offset of threadblock
            TensorCoord const& threadblock_offset)
            : iterator_(params, problem_size, scale_pointer, bias_pointer,
                        thread_id,
                        layout::PitchLinearCoord(threadblock_offset.column(),
                                                 threadblock_offset.row())) {}

    /// Construct a PredicatedTileAccessIterator with zero threadblock offset
    CUTLASS_HOST_DEVICE
    PredicatedScaleBiasVectorAccessIterator(
            Params const& params,  ///< Precomputed parameters object
            Conv2dProblemSize const& problem_size,  ///< Extent of tensor
            ConstPointer scale_pointer,  ///< Pointer to the start of the scale
                                         ///< vector
            ConstPointer
                    bias_pointer,  ///< Pointer to the start of the bias vector
            int thread_id          ///< ID of each participating thread
            )
            : PredicatedScaleBiasVectorAccessIterator(
                      params, problem_size, scale_pointer, bias_pointer,
                      thread_id, make_Coord(0, 0)) {}

    /// Overrides the internal iteration index
    CUTLASS_HOST_DEVICE
    void set_iteration_index(int index) {
        iterator_.set_iteration_index(index);
    }

    /// Advances an iterator along logical dimensions of matrix in units of
    /// whole threadblock tiles
    CUTLASS_HOST_DEVICE
    void add_tile_offset(TensorCoord const& tile_offset) {
        iterator_.add_tile_offset({tile_offset.column(), tile_offset.row()});
    }

    /// Returns a pointer
    CUTLASS_HOST_DEVICE
    AccessType* get() const {
        return reinterpret_cast<AccessType*>(iterator_.get());
    }

    /// Advances to the next tile in memory.
    ///
    /// The first time this method is called, predicates are updated, and the
    /// iterator's internal pointer is reverted to the first "steady state"
    /// tile. Subsequent calls are lightweight and must only update the internal
    /// pointer.
    CUTLASS_HOST_DEVICE
    PredicatedScaleBiasVectorAccessIterator& operator++() {
        ++iterator_;
        return *this;
    }

    /// Advances to the next tile in memory.
    ///
    /// The first time this method is called, predicates are updated, and the
    /// iterator's internal pointer is reverted to the first "steady state"
    /// tile. Subsequent calls are lightweight and must only update the internal
    /// pointer.
    CUTLASS_HOST_DEVICE
    PredicatedScaleBiasVectorAccessIterator operator++(int) {
        PredicatedScaleBiasVectorAccessIterator self(*this);
        operator++();
        return self;
    }

    /// Increment and return an instance to self.
    CUTLASS_HOST_DEVICE
    void advance() { iterator_.advance(); }

    /// Returns whether access is valid or not
    CUTLASS_HOST_DEVICE
    bool valid() { return iterator_.valid(); }
};

////////////////////////////////////////////////////////////////////////////////

}  // namespace threadblock
}  // namespace conv
}  // namespace cutlass

////////////////////////////////////////////////////////////////////////////////
